Instructions to use Casesar/bysz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Casesar/bysz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Casesar/bysz") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Casesar/bysz") model = AutoModelForImageClassification.from_pretrained("Casesar/bysz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e06c7ed61539a271319d3c43d1c4f19cdcb09e9e7ce86c56d21bc94dbc726b91
- Size of remote file:
- 10.5 MB
- SHA256:
- 899b3b165e727acfd9447ccbe0954973f2ac28d1a2620475d3cd007283dc10eb
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